Autonomous driving is a pipedream without automated, reproducible and explainable machine learning pipelines

Autonomous vehicles are set to change the future of global transportation: reducing congestion, road accidents and carbon dioxide emissions with some estimates predicting the autonomous vehicle market growing almost tenfold between 2019 and 2026.

In order to succeed with autonomous vehicles AI teams need to overcome both technical and societal challenges, and the two are intertwined. To convince the public and regulators that autonomous vehicles are safe it is important to be able to reproduce failures and redeploy updated models quickly. The same need for reproducibility impacts time-to-market, with machine learning engineers needing to reproduce the work of others in order to collaborate efficiently when training computer vision models on petabyte scale training sets.

73% of U.S. adults surveyed saying they would be too afraid to ride in a fully self-driving vehicle, up from 63% in a similar survey taken in late 2017.

How can Dotscience help?

Dotscience gives your AI teams the tools and processes they need to handle massive data and end to end model lifecycle management, while providing both insight for managers & customers, and oversight for auditors & regulators.

Dotscience gives your AI teams the tools and processes they need to handle massive data and end to end model lifecycle management, while providing both insight for managers & customers, and oversight for auditors & regulators.